1. Field of Disclosure
The disclosure generally relates to the field of controlling motion of a system, and more specifically to generating dynamically feasible motion of a system.
2. Description of the Related Art
The emergence of the field of humanoid robotics in the last decade is largely attributed to the expectation that humanoid robots will eventually become an integral part of our everyday lives, serving as caretakers for the elderly and disabled, providing assistance in homes and offices, and assisting in surgery and physical therapy. From a control perspective, much effort has been aimed at addressing various aspects of humanoid robot control, such as motion execution, safety, constraint handling, multi-contact control, balance control, and obstacle avoidance. See C. Kemp et al., “Springer Handbook of Robotics” (Chapter 56: Humanoids) (2008), B. Siciliano and O. Khatib, Eds., the content of which is incorporated by reference herein in its entirety. Although the utility of robots operating in human environments rests, to a large extent, on execution of upper-body motion and manipulation tasks, the ability to sustain dynamic balance in response to upper-body motion is an important problem that is unique to humanoid robotics research.
Whole-body motion from upper-body task specifications has been examined by several research groups and significant advances have been reported. Given that the hardware platform of many humanoid robots is designed for position control, the majority of past and present developments in whole-body motion control have been centered around kinematic and inverse kinematic control techniques. For example, one approach considered the problem of off-line motion editing when transferring pre-recorded motion from a human to a humanoid robot under the assumption of a single support stance. See N. Naksuk et al., “Whole-body human-to-humanoid motion transfer”, IEEE/RAS International Conference on Humanoid Robots (2005), pp. 104-109, the content of which is incorporated by reference herein in its entirety. The proposed scheme yielded a balanced humanoid motion with minimal angular momentum at the center of mass. Another approach generates whole-body motion from upper-body human motion capture using a marker system. See C. Ott et al., “Motion capture based human motion recognition and imitation by direct marker control,” IEEE/RAS International Conference on Humanoid Robots (2008), pp. 399-405, the content of which is incorporated by reference herein in its entirety. However, these approaches do not handle highly dynamic motion since they use a constant center of mass/gravity position centered inside the support polygon in their balance controllers.
Another approach utilizes a full-body dynamic filter developed to convert a physically infeasible reference motion into a feasible one. See K. Yamane et al., “Dynamics filter—concept and implementation of online motion generator for human figures,” IEEE Transactions on Robotics and Automation, vol. 19, no. 3, pp. 421-432, (June 2003), the content of which is incorporated by reference herein in its entirety. However, this approach alters all input motion, including the specified upper-body motion, and is not applicable where upper-body task specifications must be preserved. Furthermore, this approach requires careful parameter tuning (feedback gains and weights for pseudo-inverses) for each behavior.
Hence, there is lacking, inter alia, a system and method for generating dynamically feasible whole-body motion of a humanoid robot while realizing specified upper-body motion.